The Clinical and Translational Neuroscience Branch continues to make advances on several fronts in order to delineate the neurochemical, neurogenetic, and neuropsychological contributions to neural systems function and development relevant to mental illness. We have embarked on data collection for two unprecedented scientific resources: first, a unique multimodal neuroimaging dataset in adults that includes neuropsychological testing, extensive dopaminergic PET imaging as well as functional and structural MRI; and, second, a longitudinal, neurodevelopmental dataset that incorporates structural and functional magnetic resonance-based brain imaging, neuropsychological measures, and, in conjunction with the Section on Behavioral Endocrinology, precise, state-of-the-art endocrinological measurements of pubertal status. These comprehensive ongoing data acquisition efforts have resulted in a growing repository of integrated, multimodal information about the brain, which will permit both novel analyses synthesizing disparate but interrelated indices of neurochemical (e.g., dopamine, GABA, and glutamate) functioning and discovery of critical genetic and endocrinological factors guiding neurodevelopment. Further progress this year has been multifactorial, with particular accomplishments in the area of neuroimaging genetics, in which an array of creative strategies, including genome-wide and targeted single SNP approaches, has been employed to discern specific neurogenetic mechanisms relevant to psychiatric illness. For instance, in a large cohort of healthy individuals of European ancestry, we found that the cumulative amount of Neanderthal-derived genetic variants harbored by an individual predicts expected incremental differences in parietal and occipital bone shapes resembling those of known Neanderthal cranial remains. We also identified correlates of this Neanderthal genetic load in visual cortex and intraparietal sulcus gray- and white-matter volume, sulcal depth, and gyrification. This work suggests that Neanderthal DNA variation may be functional in modern humans and may be particularly relevant to molecular mechanisms underlying the structure and function of parietal locales that we and other groups have found to be of particular importance in select developmental and neurological disorders. Additionally, in a collaborative study with the Cognitive Genomics Consortium (COGENT), a genome-wide search for association with general cognitive function in 35,298 healthy individuals of European descent across 24 cohorts yielded two new highly significant cognition-linked loci, measured heritability of over 20% for general cognitive function, and identified polygenic correlations between cognitive performance and other phenotypes, including psychiatric disorders. These data demonstrate the power of collaborative, data-driven genome-wide approaches but do not overshadow the invaluable nature of targeted, hypothesis-driven studies. For instance, in light of convergent effects of ovarian steroid hormones and brain-derived neurotrophic factor (BDNF) on brain function, we conducted PET and fMRI studies of working memory in healthy women undergoing a 6-month hormone manipulation protocol during three conditions: pharmacologically-induced ovarian suppression, ovarian suppression plus estradiol; and ovarian suppression plus progesterone. We found an ovarian hormone-by-BDNF interaction on working memory-related hippocampal function owing to differential hippocampal recruitment in Met carriers but only in the presence of estradiol. These data are the first to suggest that human hippocampal function in Met carriers is particularly sensitive to ovarian steroids, similar to prior observations of BDNF-by-estrous cycle interactions observed in mice. In so far as BDNF is an important modulator of constitutive stress responses, these data may ultimately prove relevant to anxiety-related phenotypes. Indeed, in an independent cohort, we have used rCBF PET to identify BDNF-associated differences in relationships between rCBF and trait anxiety in limbic/paralimbic regions, including the hippocampal/parahippocampal region. Additionally, this year, productivity in the area of neuroimaging methods development, which is important in ensuring sensitivity of our assays and replicability of our and field-wide results, has taken the form of application of machine learning algorithms to artifact screening and quality assessment of neuroimaging data, tasks generally requiring operator visual inspection, as well as an innovative approach to improving signal-to-noise ratios in MRS data by correction for drift in the frequency offset of the measured region due to small changes in the scanner magnetic field and patient movement over time.